تلفیق اطلاعات ژیروسکوپ و مغناطیس‌سنج برای تخمین وضعیت پرتابه‌های با سرعت بالا بر مبنای ترکیب فیلتر ذره‌ای و PSO

نوع مقاله : گرایش دینامیک، ارتعاشات و کنترل

نویسندگان

1 مجتمع برق و کامپیوتر ، دانشگاه صنعتی مالک اشتر، تهران،ایران

2 مجتمع برق و کامپیوتر، دانشگاه مالک اشتر، تهران ایران

چکیده

در این پژوهش، با استفاده از تلفیق خروجی مغناطیس‌سنج‌ها و ژیروسکوپ‌های ارزان قیمت میکرو الکترومکانیکی، وضعیت پرتابه‌های با سرعت بالا با استفاده از فیلتر ذره‌ای ترکیب‌شده با الگوریتم بهینه‌سازی انبوه ذرات تخمین­زده می‌شود. در سرعت‌های بالا ژیروسکوپ‌های ارزان قیمت سامانه‌های میکرو الکترومکانیکی دارای خطای زیادی هستند و مغناطیس‌سنج‌ها نیز به­دلیل وجود میدان‌های مغناطیسی غیر از زمین دقت پایینی دارند. برای حل این مشکلات، تلفیق اطلاعات این دو حسگر برای تخمین وضعیت پیشنهاد می‌شود. به دلیل غیرخطی بودن معادلات دینامیکی و مشاهده باید از تخمین­گر غیرخطی استفاده ‌شود. فیلتر کالمن توسعه‌یافته با صرف‌نظر نمودن از جملات مرتبه بالای بسط تیلور خطایی را وارد محاسبات می‌نماید که این خطا در سامانه‌های غیرخطی سریع قابل صرف‌نظر نیست. فیلتر ذره‌ای برخلاف فیلتر کالمن، برای سامانه‌های غیرخطی نتایج خوبی دارد. بزرگترین ضعف این فیلتر، بار محاسباتی بالای آن است که باعث محدودیت کاربردش گشته است. برای کاهش مدت‌زمان انجام محاسبات فیلتر ذره‌ای از الگوریتم ترکیب‌شده فیلتر ذره‌ای با الگوریتم بهینه‌سازی انبوه ذرات استفاده شده است. نتایج شبیه‌سازی با استفاده از 100 نمونه آزمایش مورد بررسی قرار گرفته است که نشان‌دهنده عملکرد مطلوب این الگوریتم در مسئله تلفیق اطلاعات ژیروسکوپ و مغناطیس‌سنج در تخمین زوایا است.

کلیدواژه‌ها

عنوان مقاله [English]

Data Fusion of Gyroscope and Magnetometer to Estimate the Attitude of High-Speed Projectiles Based on Particle Filter and PSO

نویسندگان [English]

  • Ali Asghari 1
  • Saeed Nasrollahi 2
  • Nematollah Ghahremani 1
1 Department of Electrical and Computer Engineering Malek-Ashtar University of Technology, Tehran,Iran
2 Department of Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, Iran
چکیده [English]

In this paper, the attitude of high-speed projectiles has been estimated usingdata fusion of magnetometer and Micro Electro Mechanical Systems (MEMS) gyroscope. MEMS gyroscopes have the high error for high speed. Also, magnetometers have low accuracy due to the presence of Non-Earth magnetic fields. For this reason, data fusion of magnetometer and MEMS gyroscope have been suggested. Due to the nonlinearity of the system equations and observation, a nonlinear estimator must be used. The developed Kalman filter inserts an error by ignoring the high order sentences of Taylor's expansion, which cannot be ignored in fast nonlinear systems. Unlike the Kalman filter, the particle filter has good results for nonlinear systems. The biggest weakness of this filter is its high computational time, which limits its applicability. To reduce the computational time of particle filter, a particle swarm optimization algorithm has been used. The simulation results were evaluated using 100 samples of the test, which illustrates the desirable performance of the combined particle filter with the particle swarm optimization algorithm in the data fusion of gyroscope and magnetometer information in the estimation of angles.

کلیدواژه‌ها [English]

  • Particle swarm optimization algorithm
  • Attitude estimation
  • Gyroscope
  • Particle filter
  • Magnetometer

مراجع

  1.  Guo C, Cai H, Hu Z, “Nonlinear Filtering Techniques for Geomagnetic Navigation,” Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 228, no. 2, pp. 305–320, 2014.##
  2. Park SG, Jeong HC, Kim JW, Hwang D-H, Lee SJ, “Magnetic Compass Fault Detection Method for GPS/INS/Magnetic Compass Integrated Navigation Systems,” Int. J. Control. Autom. Syst., vol. 9, no. 2, p. 276, 2011.##
  3. Rogers J, Costello M, Harkins T, Hamaoui M, “Effective Use of Magnetometer Feedback for Smart Projectile Applications,” Navigation, vol. 58, no. 3, pp. 203–219, 2011.##
  4. Zhao W, Bu X, Yu G, Xiang C, “Feedback-Type Giant Magneto-Impedance Sensor Based on Longitudinal Excitation,” J. Magn. Magn. Mater., vol. 324, no. 19, pp. 3073–3077, 2012.##
  5. Včelák J, Kub’ik J, “Influence of Sensor Imperfections to Electronic Compass Attitude Accuracy,” Sensors Actuators A Phys., vol. 155, no. 2, pp. 233–240, 2009.##
  6. Long DF, Lin J, Zhang XM, Li J, “Orientation Estimation Algorithm Applied to High-Spin Projectiles,” Meas. Sci. Technol., vol. 25, no. 6, p. 65001, 2014.##
  7. Xiang C, Bu XZ, Zhu YP, “Design of New Attitude Measuring Method of Non-Spinning Projectile Based on Magnetic Sensors,” in Applied Mechanics and Materials, 2012, vol. 226, pp. 1825–1828.##
  8. Xiang C, Bu X, Yang B, “Three Different Attitude Measurements of Spinning Projectile Based on Magnetic Sensors,” Measurement, vol. 47, pp. 331–340, 2014.##
  9. Li D, Bu X-Z, “Roll Angle Measurement of Spinning Projectile Based on Non-orthogonal Magnetic Sensors,” Acta Armamentarii, vol. 31, no. 10, pp. 1316–1321, 2010.##
  10. Yu J, Bu X, Xiang C, Yang B, “Spinning Projectile’s Attitude Measurement Using Intersection Ratio of Magnetic Sensors,” Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 231, no. 5, pp. 866–876, 2017.##
  11. Hepner DJ, Harkins TE, “Determining Inertial Orientation of a Spinning Body with Body-Fixed Sensors,” in Acquisition, tracking, and pointing XIV, 2000, vol. 4025, pp. 68–78.##
  12. Harkins TE, Hepner DJ, “MAGSONDE: A Device for Making Angular Measurements on Spinning Projectiles with Magnetic Sensors,” 2000.##
  13. Harkins TE, Davis BS, Hepner DJ, “Novel Onboard Sensor Systems for Making Angular Measurements on Spinning Projectiles,” in Acquisition, Tracking, and Pointing XV, 2001, vol. 4365, pp. 176–187.##
  14. Li D, Bu X-Z, “Attitude Measurement on High-Spinning Projectile Using Magnetic Sensors and Accelerometers,” Trans. Nanjing Univ. Aeronaut. Astronaut., vol. 25, no. 2, pp. 106–112, 2008.##
  15. Liu X, “An Improved Interpolation Method for Wind Power Curves,” IEEE Trans. Sustain. Energy, vol. 3, no. 3, pp. 528–534, 2012.##
  16. Li X, Li Z, “A New Calibration Method for Tri-axial Field Sensors in Strap-down Navigation Systems,” Meas. Sci. Technol., vol. 23, no. 10, p. 105105, 2012.##
  17. Yu H, Honglun W, “Application of Tracking-Differentiator in Angular Measurements on Spinning Projectiles Using Magnetic Sensors,” in 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2015, vol. 1, pp. 433–436.##
  18. Zhu J, Wu P, Bo Y, “A Novel Attitude Estimation Algorithm Based on the Non-orthogonal Magnetic Sensors,” Sensors, vol. 16, no. 5, p. 730, 2016.##
  19. Qi KY, Xiang C, Bu XZ, Yu J, “Analysis of Theory and Model of Bbackground Magnetic Field of High-spinning Projectile,” in Applied Mechanics and Materials, 2014, vol. 556, pp. 1954–1958.##
  20. Yu J, Bu X, Xiang C, Wang X, “Spinning Projectile’s Attitude Measurement Using Background Magnetic Field Compensation,” J. Appl. Remote Sens., vol. 10, no. 1, p. 14001, 2016.##
  21. Zhu R, Sun D, Zhou Z, Wang D, “A Linear Fusion Algorithm for Attitude Determination Using Low Cost MEMS-based Sensors,” Measurement, vol. 40, no. 3, pp. 322–328, 2007.##
  22. Zeng Z, Zhang S, Xing Y, Cao X, “Robust Adaptive Filter for Small Satellite Attitude Estimation Based on Magnetometer and Gyro,” in Abstract and Applied Analysis, 2014, vol. 2014.##
  23. Miao C, Zhang Q, Fang J, Lei X, “Design of Orientation Estimation System by Inertial and Magnetic Sensors,” Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., vol. 228, no. 7, pp. 1105–1113, 2014.##
  24. Mohammad-Hoseni S, Seifi, M, “Error Rate Reduction of a Low-Cost Integrated Navigation System Using Neural Networks,” J. Mech. Aerosp., vol. 15, no. 3, pp. 305-320(In Persian).##
  25. Li C, Wang L, Li X, “Method of Attitude-Aided magnetometers/SINS/GNSS integration,” in 2013 6th international conference on information management, innovation management and industrial engineering, 2013, vol. 1, pp. 304–308.##
  26. Zhao H, Wang Z, “Motion Measurement Using Inertial Sensors, Ultrasonic Sensors, and Magnetometers with Extended Kalman Filter for Data Fusion,” IEEE Sens. J., vol. 12, no. 5, pp. 943–953, 2011.##
  27. Kao CF, Chen TL, “Design and Analysis of an Orientation Estimation System Using Coplanar Gyro-free Inertial Measurement Unit and Magnetic Sensors,” Sensors actuators A Phys., vol. 144, no. 2, pp. 251–262, 2008.##
  28. Simon D, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. 2006.##
  29. Arulampalam B, Beyond the Kalman Filter: Particle Filters for Tracking Applications. 2004.##
  30. Havangi R, Teshnehlab M, Nekoui MA, Taghirad H, “A Study of Estimation Problem from Viewpoint of Conditional Optimization and Designing of Evolutionary Estimator,” Aerosp. Mech. J, vol. 7, no. 1, pp. 27-40(In persian).##
  31. Higuchi T, “Monte Carlo Filter Using the Genetic Algorithm Operators,” J. Stat. Comput. Simul., vol. 59, no. 1, pp. 1–23, 1997.##
  32. Kwok NM, Fang G, Zhou W, “Evolutionary Particle Filter: Re-sampling from the Genetic Algorithm Perspective,” in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005, pp. 2935–2940.##
  33. Park S, Hwang J, Rou K, Kim E, “A New Particle Filter Inspired by Biological Evolution: Genetic Filter,” World Acad. Sci. Eng. Technol., vol. 33, pp. 83–87, 2007.##
  34. Tong G, Fang Z, Xu X, “A Particle Swarm Optimized Particle Filter for Nonlinear System State Estimation,” in 2006 IEEE International Conference on Evolutionary Computation, 2006, pp. 438–442.##
  35. Zheng Y, Meng Y, “Swarming Particles with Multi-feature Model for Free-selected Object Tracking,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 2553–2558.
  36. Havangi R, Nekoui MA, Teshnehlab M, “A Multi Swarm Particle Filter for Mobile Robot Localization,” Int. J. Comput. Sci., vol. 7, no. 3, pp. 15–22, 2010.##
  37. Pang H, Pan M, Chen J, Li J, Zhang Q, Luo S, “Integrated Calibration and Magnetic Disturbance Compensation of Three-axis Magnetometers,” Measurement, vol. 93, pp. 409–413, 2016.##
  38. Kiani M, Pourtakdoust SH, Sheikhy AA, “Consistent Calibration of Magnetometers for Nonlinear Attitude Determination,” Measurement, vol. 73, pp. 180–190, 2015.##
  39. Yu J, Ding F, Zhao X, Zhou F, “Error Compensation Method of Magnetometer for Attitude Measurement Using Modified Artificial Bee Colony Algorithm,” in 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 2, pp. 348–351.##
  40. Söken HE, Sakai S, “Real-time Attitude-Independent Magnetometer Bias Estimation for Spinning Spacecraft,” J. Guid. Control. Dyn., vol. 41, no. 1, pp. 276–279, 2017.##
  41. Abiri, A, Mahzoun, MR, “Aerial Moving Target Tracking using Kernel Density Estimation Based on Particle Filter Algorithm,” Tabriz J. Electr. Eng Persian, vol. 45, no. 3, p. (In Persian).##
  42. Eberhart R, Kennedy J, “A New Optimizer Using Particle Swarm Theory,” Hum. Sci. 1995. MHS’95.,  …, 1995.##
  43. Juárez-Castillo E, Acosta-Mesa H-G, Mezura-Montes E, “Empirical Study of Bound Constraint-handling Methods in Particle Swarm Optimization for Constrained Search Spaces,” in 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, pp. 604–611.##
  44. Zhang G, Cheng Y, Yang F, Pan Q. Pan, “Particle Filter Based on PSO,” in 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), 2008, vol. 1, pp. 121–124.##
  45. VCC DIO, “Programmable Digital Gyroscope Sensor Data Sheet ADIS16260ADIS16265,” vol., no., p. .##
  46. Devices A, “DEVICES, Analog. ADIS16266 datasheet. USA: Analog Devices,” vol., no., p. .##
  47. Jacobs JA, “Geomagnetism. Vol. 4.,” in Geomagnetism, 1991, vol. 4.##
  48. Jacobs JA, “Sensor Product. HMC1001/1002 datasheet,” Honeywell, vol., no., p. .##

فایل ها

سابقه مقاله

  • تاریخ دریافت: 05 خرداد 1398
  • تاریخ بازنگری: 01 تیر 1398
  • تاریخ پذیرش: 27 شهریور 1398
  • تاریخ انتشار: 01 فروردین 1399

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